Hydrogen refueling stations (HRSs) are critical nodes coupling power

hydrogen

and transportation networks. Their operation faces a dual challenge: multi-network chain reactions triggered by spatiotemporal clustering of traffic demand

and the risk of thermal-stress failure in storage tanks due to rapid discharge during peak hours. To address these issues

this paper proposes a coordinated multi-network optimization model that integrates refueling price-based traffic guidance with thermal safety constraints for hydrogen storage. A dynamic temperature model and its surrogate are developed to capture the tank's thermal behavior. Furthermore

a bi-level model designed to mitigate traffic congestion is transformed into a Mathematical Program with Equilibrium Constraints (MPEC) using a variational inequality. This approach circumvents the "zero-gradient" problem inherent in bi-level models and avoids the high dimensionality of traditional KKT transformations. The large-scale

non-convex problem is decomposed via the Alternating Direction Method of Multipliers (ADMM)

and the resulting nonlinear transportation subproblem is efficiently solved by a hybrid algorithm combining a gap function with sequential quadratic programming (GF-SQP). Numerical studies validate the framework's effectiveness in achieving safe

cost-effective system co-optimization.